Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Working in an interactive learning environment, led by our engaging expert, you will:
Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions.
Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4.
Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements.
Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases.
Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions.
Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex.
Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development.

1
  • INTRODUCTION TO PROMPT ENGINEERING

  • Overview of prompt engineering and its importance in AI applications

    Major applications of prompt engineering in business

    Common challenges faced in prompt engineering

    Overview of GPT-4 and its role in prompt engineering

    Key terminology and concepts in prompt engineering


2
  • GETTING THINGS READY: TEXT PREPROCESSING AND DATA CLEANSING

  • Importance of data preprocessing in prompt engineering

    Techniques for text cleaning and normalization

    Tokenization and n-grams

    Stop word removal and stemming

    Regular expressions and pattern matching


3
  • GPT-4 TOKENIZATION AND INPUT FORMATTING

  • GPT-4 tokenization and its role in prompt engineering

    Understanding and formatting GPT-4 inputs

    Context windows and token limits

    Controlling response length and quality

    Techniques for handling out-of-vocabulary tokens


4
  • PROMPT DESIGN AND OPTIMIZATION

  • Master the skills to design, optimize, and test prompts for various business tasks.

    Designing effective prompts for different tasks

    Techniques for prompt optimization

    GPT-4 system and user parameters for controlling behavior

    Importance of prompt testing and iteration

    Best practices for prompt engineering in business applications


5
  • ADVANCED TECHNIQUES AND TOOLS IN PROMPT ENGINEERING

  • Learn advanced techniques and tools for prompt engineering and their integration in business applications.

    Conditional text generation with GPT-4

    Techniques for handling multi-turn conversations

    Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground

    Integration of GPT-4 with other software platforms and tools

    Monitoring and maintaining prompt performance


6
  • CODE GENERATION AND TESTING WITH PROMPT ENGINEERING

  • Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects.

    Introduction to code generation with AI models like GPT-4

    Designing prompts for code generation across programming languages

    Techniques for specifying requirements and constraints in prompts

    Generating and interpreting code snippets using AI-driven solutions

    Integrating generated code into existing projects and codebases

    Best practices for testing and validating AI-generated code


7
  • ETHICS AND RESPONSIBLE AI

  • Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business.

    Ethical considerations in prompt engineering

    Bias in AI systems and its impact on prompt engineering

    Techniques to minimize bias and ensure fairness

    Best practices for responsible AI deployment in business applications

    Monitoring and addressing ethical concerns in prompt engineering


Audience

To gain the most from attending this course you should possess the following incoming skills: Basic knowledge of programming concepts and syntax in Python. Familiarity with common data formats such as CSV, JSON, and XML. Experience using command-line interfaces and basic text editing tools. Understanding of basic machine learning concepts and algorithms.

Language

English

Prerequisites

You should have incoming skills aligned with the topics in the course(s) below, or should attend as a pre-requisite: TTML5503 Introduction to AI, AI Programming & Machine Learning (3 days) TTPS4873 Fast Track to Python in Data Science (3 days)

$895

Length: 1.0 day (8 hours)

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